Abstract
As a result of the pet trade, Africa’s Nile monitor (Varanus niloticus) is now established in North America (Florida). This generalist carnivore is a potential threat to native wildlife, requiring proactive measures to effectively prevent further spread into novel regions. To determine regions at risk, we create and compare alternative ensemble species distribution models (SDMs) using a model selection approach (with 10 possible modeling algorithms grouped according to assumptions). The ensemble SDMs used presence and environmental data from both native (Africa) and nonnative (Florida) locations. The most predictive consensus SDMs for native and native + nonnative data sets (TSS = 0.87; Sensitivity = 93%; Specificity = 94%) were based on the boosted regression tree (BRT), classification tree analysis (CTA), and random forest (RF) modeling algorithms with all environmental predictor variables used. The global Nile monitor SDMs predict strong habitat suitability in tropical and subtropical regions in the Americas, the Caribbean, Madagascar, Southeast Asia, and Australia. Florida Nile monitor populations are less likely to spread into the Neotropics than if pets now in the Southwest USA are released intentionally or accidentally. Management options to avoid this spread into vulnerable regions are to actively prohibit/regulate Nile monitors as pets, enforce those restrictions, and promote exotic pet amnesty programs. The model selection approach for ensemble SDMs used here may help improve future SDM research
Highlights
Successful vertebrate invaders often exhibit a combination of the following traits: close association with humans, abundance in a wide native range, competitive nature, large size, broad diet, high tolerance to various physical conditions, and rapid reproduction (Ehrlich 1989, Sakai et al 2001, Sol 2008)
The most informative model hypothesis based on native Nile monitor presence data included all three predictor variables— Climate + Vegetation + Elevation —and the boosted regression tree (BRT), classification tree analysis (CTA), and random forest (RF) algorithms (PA Group C; true skill statistic (TSS) = 0.87; Table 2 and 3)
Our final global ensemble species distribution models (SDMs) produced highly accurate and robust projections based on all three predictor variables, both sets of presence data available, and were optimized using the PA Group C algorithms to create the most informative predictions for Nile monitor habitat suitability across the globe (Table 3)
Summary
Successful vertebrate invaders often exhibit a combination of the following traits: close association with humans, abundance in a wide native range, competitive nature, large size, broad diet, high tolerance to various physical conditions, and rapid reproduction (Ehrlich 1989, Sakai et al 2001, Sol 2008). Ssali 1983; Losos and Greene 1988, Lenz 1995, Luiselli et al 1999, Faust 2001, Bayless 2002, Bennett 2002, de Buffrenil and Hemery 2002, Ciliberti et al 2012) These characteristics cause the Nile monitor to become a potential invasive threat if introduced to new areas across the globe, in areas where native top predators have been diminished or exterminated. These concerns are further exacerbated by its popularity in the exotic pet industry, potentially allowing further introductions This Old-World monitor has already been established in southern Florida since ~1990 due to the global pet trade (Campbell 2003, Enge et al 2004, Campbell 2005), raising concerns about where else it might become established if introduced, and where it might become invasive once established.
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